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Distance measures for tumor evolutionary trees

MOTIVATION: There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference method...

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Detalles Bibliográficos
Autores principales: DiNardo, Zach, Tomlinson, Kiran, Ritz, Anna, Oesper, Layla
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141873/
https://www.ncbi.nlm.nih.gov/pubmed/31750900
http://dx.doi.org/10.1093/bioinformatics/btz869
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author DiNardo, Zach
Tomlinson, Kiran
Ritz, Anna
Oesper, Layla
author_facet DiNardo, Zach
Tomlinson, Kiran
Ritz, Anna
Oesper, Layla
author_sort DiNardo, Zach
collection PubMed
description MOTIVATION: There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology. RESULTS: Here, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. AVAILABILITY AND IMPLEMENTATION: Implementations of CASet and DISC are freely available at: https://bitbucket.org/oesperlab/stereodist. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-71418732020-04-13 Distance measures for tumor evolutionary trees DiNardo, Zach Tomlinson, Kiran Ritz, Anna Oesper, Layla Bioinformatics Original Papers MOTIVATION: There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology. RESULTS: Here, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. AVAILABILITY AND IMPLEMENTATION: Implementations of CASet and DISC are freely available at: https://bitbucket.org/oesperlab/stereodist. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-04-01 2019-11-21 /pmc/articles/PMC7141873/ /pubmed/31750900 http://dx.doi.org/10.1093/bioinformatics/btz869 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Papers
DiNardo, Zach
Tomlinson, Kiran
Ritz, Anna
Oesper, Layla
Distance measures for tumor evolutionary trees
title Distance measures for tumor evolutionary trees
title_full Distance measures for tumor evolutionary trees
title_fullStr Distance measures for tumor evolutionary trees
title_full_unstemmed Distance measures for tumor evolutionary trees
title_short Distance measures for tumor evolutionary trees
title_sort distance measures for tumor evolutionary trees
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141873/
https://www.ncbi.nlm.nih.gov/pubmed/31750900
http://dx.doi.org/10.1093/bioinformatics/btz869
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